Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning
| العنوان: | Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning |
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| المؤلفون: | Makoto Iwasaki, Junya Kanda, Yasuyuki Arai, Tadakazu Kondo, Takayuki Ishikawa, Yasunori Ueda, Kazunori Imada, Takashi Akasaka, Akihito Yonezawa, Kazuhiro Yago, Masaharu Nohgawa, Naoyuki Anzai, Toshinori Moriguchi, Toshiyuki Kitano, Mitsuru Itoh, Nobuyoshi Arima, Tomoharu Takeoka, Mitsumasa Watanabe, Hirokazu Hirata, Kosuke Asagoe, Isao Miyatsuka, Le My An, Masanori Miyanishi, Akifumi Takaori-Kondo |
| المصدر: | Blood Advances. 6(8):2618-2627 |
| بيانات النشر: | American Society of Hematology, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Adult, Machine Learning, Transplantation, Recurrence, Risk Factors, Chronic Disease, Hematopoietic Stem Cell Transplantation, Humans, Hematology, Middle Aged, Disease-Free Survival, Retrospective Studies |
| الوصف: | Graft-versus-host-disease-free, relapse-free survival (GRFS) is a useful composite endpoint that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT at the Kyoto Stem Cell Transplantation Group (KSCTG), a multi-institutional joint research group of 17 transplantation centers in Japan. The primary endpoint was GRFS. A stacked ensemble of Cox proportional hazard regression and seven machine learning algorithms was applied to develop a prediction model. The median age of patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other top-of-the-art competing risk models (ensemble model: 0.670, Cox-PH: 0.668, Random Survival Forest: 0.660, Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk and 40.69% for the low-risk group, respectively (hazard ratio [HR] compared to the low-risk group: 2.127; 95% CI: 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine learning algorithms. アンサンブル学習を用いた造血幹細胞移植予後予測モデルの開発 --機械学習を用いた新規生存時間解析手法の実装--. 京都大学プレスリリース. 2021-12-28. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2473-9529 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b11500b0fffc3b21ac163633b6429c6 http://hdl.handle.net/2433/269453 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4b11500b0fffc3b21ac163633b6429c6 |
| قاعدة البيانات: | OpenAIRE |
Full Text via ClinicalKey | تدمد: | 24739529 |
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